Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels

Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content.

We propose a graph-based framework for identifying and analyzing disinformation narratives in Telegram ecosystems by combining weak supervision with propagation graph analysis.

The approach aggregates semantically related claims into narrative-level clusters and models their diffusion across interconnected channels.

This enables the detection of coordinated narrative amplification that is difficult to capture through post-level analysis alone.

Our results demonstrate that integrating textual signals with network structure provides a scalable method for detecting disinformation narratives and offers insights into how they propagate within large-scale messaging environments.